ESRI Discussion Paper Series No.389

Market Power and Labor Shares : Evidence from JapaneseFirm-Level Data

Ryoya Nagao
Yoshiyuki Nakazono
Kento Tango
March 2024

Economic and Social Research InstituteCabinet Office
Tokyo, JapanThe views expressed in “ESRI Discussion Papers” are those of the authors and not those of
the Economic and Social Research Institute, the Cabinet Office, or the Government of Japan.
(Contact us: https://form.cao.go.jp/esri/en_opinion-0002.html)

Market Power and Labor Shares:
Evidence from Japanese Firm-Level Data*
Ryoya Nagao†

Yoshiyuki Nakazono‡

Kento Tango§February 29, 2024

Abstract
We analyze the association of market power with labor share in Japan. We ﬁrst estimate
markups as a proxy of market power and present the evidence on the patterns of markups. We
ﬁnd that aggregate markups were stable. This suggests no superstar ﬁrm effects, which Autor etal. (2020) ﬁnd in the United States. While markups are stable at the aggregate level, we present
evidence on the microeconomic dynamics of markups. We ﬁnd the association of markups withﬁrms’ demographics: markups are negatively associated with ﬁrm age and size; thus, markups arehigher for young and small ﬁrms. We also ﬁnd the patterns of markups at the semi-macro level:
markups are higher for nonmanufacturing industries than manufacturing industries. We then explore the macroeconomic implications of the patterns of markups. We show that the rise in market
power decreases the labor share and increases the capital share.

JEL Classiﬁcation:

D22; D24; D40; E31

Keywords:

capital share; markup; labor share; market power* We thank Hirokazu Ishise, Qing-Yuan Sui, Michio Suzuki, and participants in the Japanese Joint Statistical Meetingfor their comments and suggestions. Nakazono acknowledges ﬁnancial support from the Institute of Social and Economic
Research, Osaka University, and JSPS KAKENHI Grant Numbers 21H04397, 22K01438, and 23K17553. Tango acknowledges ﬁnancial support from JST, the establishment of university fellowships towards the creation of science technology
innovation, Grant Number JPMJFS 2140.
†
Yokohama City University‡
Yokohama City University§
Yokohama City University

ESRI Discussion Paper Serires No.389"Market Power and Labor Shares : Evidence from Japanese Firm-Level Data"

1 Introduction
A well-functioning economy requires vigorous competition between ﬁrms. The lack of competition enables ﬁrms to gain power, charge high prices, and lower consumer well-being. Additionally,market power decreases the demand for labor and dampens investment in capital. It distorts the
distribution of economic rents and discourages business dynamics and innovation. This has ramiﬁcations for policy, from antitrust to monetary policy and income redistribution.
Despite the signiﬁcance of market power in economics, less is known about its systematic
patterns for the Japanese economy because measuring market power is difﬁcult (De Loecker et al.,2020). To measure market power, we follow the approach proposed by De Loecker et al. (2020)and estimate markups as a proxy of market power.
Markups are the most common measure of whether ﬁrms can price their goods above their
marginal cost. We ﬁrst estimate markups and present empirical evidence for them. Based onJapanese ﬁrm-level data, we ﬁnd that aggregate markups are stable. It suggests no superstar ﬁrm
effects in Japan, which Autor et al. (2020) ﬁnd in the United States. Although markups are stable atthe aggregate level, we present evidence on the microeconomic dynamics of markups. We ﬁnd theassociation of markups with ﬁrms’ demographics: markups are negatively associated with ﬁrm age
and size. Thus, markups are higher for young or small ﬁrms. We also ﬁnd the patterns of markups
at the semi-macro level: markups are higher for nonmanufacturing industries than for manufacturing industries. We then discuss the macroeconomic implications of the patterns of markups.
Furthermore, we demonstrate that the increase in market power entails a decline in labor share andan increase in capital share.
This study contributes to the existing literature by providing evidence on the microeconomic
and macroeconomic dynamics of market power. First, as a proxy of market power, we document
the patterns of markups. For the United States, De Loecker et al. (2020) present that markups start
to rise, and the increase is driven primarily by the upper tail of the markup distribution, whichis consistent with the superstar ﬁrm effects that Autor et al. (2020) ﬁnd. For Japan, De Loecker
and Eeckhout (2018) ﬁnd that markups in 2000 were ﬂat. Cabinet Ofﬁce, Government of Japan(2023) and Aoki et al. (2023) report similar evidence with De Loecker and Eeckhout (2018).12 We
demonstrate no superstar ﬁrm effects in Japan and provide the new facts that markups are associatedwith ﬁrms’ demographics. Our evidence suggests that as is consistent with Porter and Sakakibara
1

Ariga (1999) document that the distribution of markups in Japan is right skewed.
Nishioka and Tanaka (2019) and Kondo (2020) estimate Japanese ﬁrms’ markups while they focus on manufacturing industries and provide no evidence for nonmanufacturing industries. Furthermore, Nakamura and Ohashi (2019) andNakamura and Ohashi (2022) estimate markups to examine the relationship between a ﬁrm’s markup and its ﬁrm-to-ﬁrm
transaction status.
2

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ESRI Discussion Paper Serires No.389"Market Power and Labor Shares : Evidence from Japanese Firm-Level Data"(2006), competitiveness in Japan is ﬁerce.3
Second, this study provides evidence on the ﬁrm’s life cycle. The literature has exploredwhether ﬁrm age affects its performance and creativity for innovation. Evans (1987) demonstrates
that ﬁrm age and size are negatively related to its growth, while Acemoglu et al. (2022) show thatﬁrm age is negatively associated with innovation quality, suggesting that younger ﬁrms are morecreative. The literature also examines the relationship between the CEO’s age and the ﬁrm’s performance.4 Using data from the United States, Cline and Yore (2016) ﬁnd that CEO age is signiﬁcantly
negatively related to ﬁrm value and performance. Using data from European countries, Belenzon
et al. (2019), Cline and Yore (2016), Navaretti et al. (2014), and Coad et al. (2016) conclude thatas a CEO ages, the ﬁrm experiences lower investment, lower sales growth, and lower proﬁtability.
However, in the Japanese context, evidence on the effects of ﬁrm age on its value and performanceis scant. Yasuda (2005) ﬁnds that ﬁrm age and size negatively affect ﬁrm growth. He focuseson manufacturing industries and provides no evidence for nonmanufacturing industries.5 We contribute to the existing literature by adding evidence on the relationship between ﬁrm age and its
performance.
Third, this study provides macroeconomic implications for the relationship between markups
and labor share. Researchers have analyzed market power as a determinant of the secular declines inlabor shares. Yeh et al. (2022) examine whether the U.S. labor market is perfectly competitive anddemonstrates that the increase in market power decreases labor share. Using data from developed
countries, Akcigit et al. (2021) ﬁnd that the rise in the employer market power has adverse effects on
economic growth, investment, innovation, and labor share. Several studies in the Japanese context
report that the labor share was stable (Fukao and Perugini, 2021; Hirakata and Koike, 2018) whileevidence on the association of markups with the labor share is scant, except for Aoki et al. (2023)
and Cabinet Ofﬁce, Government of Japan (2023). We measure the ﬁrm-level markups to examine
the dynamics between the ﬁrm’s markup and the labor share. We provide micro-level evidence on
the association of markups with labor shares and discuss its macroeconomic implications.
In the remainder of this paper, Section 2 describes the Japanese ﬁrm-level data we use. Section 3
presents the estimation strategy for markups. Then, Section 4 provides the evidence on the patterns
of markups, and Section 5 shows the microeconomic dynamics between markups and the labor
share. Finally, Section 6 discusses the macroeconomic implications of our results, and Section 7
3
Sakakibara and Porter (2006) point out that domestic competition in Japan is positively correlated with performance inthe trade sector.
4
Focusing on the career concerns of the young CEO, Li et al. (2017) demonstrate that younger CEOs are more likely toenter new lines of business and exit existing ones. Using auction records, Galenson and Weinberg (2000) demonstrate thatthe relationship between art prices and ages is U-shaped.
5
In the literature on banking, Sakai et al. (2010) examine ﬁrm age and the evolution of borrowing costs.

3

ESRI Discussion Paper Serires No.389"Market Power and Labor Shares : Evidence from Japanese Firm-Level Data"

concludes.

2 DataWe use panel data between 2008 and 2020 from the Basic Survey of Japanese Business Structure
and Activities conducted by the Ministry of Economy, Trade and Industry (METI). The survey aims
to clarify the business activities of Japanese ﬁrms to obtain basic data for business-related policymaking by METI. The survey was launched in 1991 as an annual survey to obtain a collective andquantitative understanding of the actual conditions of diversiﬁcation, globalization, and internationalization of Japanese ﬁrms. It provides basic information on Japanese enterprises in terms ofﬁnancial status, enterprise composition, R&D activities, IT use, and foreign direct investment. The
survey is a mail/online survey based on self-declaration survey forms given to parent companies.
The number of sample ﬁrms is greater than 25,000 each year.
This survey focuses on companies with 50 or more employees; companies whose paid-up capital or investment fund is over 30 million yen; companies whose operation falls under the mining,
manufacturing, wholesale and retail trade, eating and drinking places, and other industries suchas electricity and gas service, information service, etc. The companies covered by the survey are
engaged in one of the following categories based on the Japan Standard Industrial Classiﬁcation:(1) Division-C: Mining and Quarrying of Stone and Gravel; (2) Division-E: Manufacturing; (3)Division-F: Electricity, Gas, Heat Supply and Water; (4) Division-G: Information and Communications; (5) Division-I: Wholesale and Retail Trade; (6) Division-J: Finance and Insurance; (7)Division-K: Real Estate and Goods Rental and Leasing; (8) Division-L: Scientiﬁc Research, Professional and Technical Services; (9) Division-M: Accommodation, Eating and Drinking Services;(10) Division-N: Living-Related and Personal Services and Amusement Services; (11) Division-O:
Education, Learning Support; and (12) Division-R: Services, not elsewhere classiﬁed. We classify Divisions C through F and Divisions F through R as the manufacturing and nonmanufacturingindustries, respectively.

3 Estimating markups
Following the so-called production function approach proposed by De Loecker et al. (2020), we
estimate markups at the ﬁrm level. Consider N ﬁrms (i = 1, . . . , N ) in the economy. Each ﬁrm’s
productivity Ωit and production technology Qit (·) are heterogeneous. Assume that at time t, ﬁrm i

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ESRI Discussion Paper Serires No.389"Market Power and Labor Shares : Evidence from Japanese Firm-Level Data"

minimizes its costs;
Qit = Qit (Ωit , Vit , Kit ),where V = (V 1 , . . . , V J ) is the vector of variable costs of labor, intermediate input goods, andmaterials for production. Kit indicates the capital stock. Ωit is productivity. We assume thatvariable costs would adjust immediately in one year, but capital takes longer to adjust. We use
the cost of sales as an aggregate rather than individual variable costs. We consider the following
Lagrangian objective function for cost minimization in a ﬁrm:
L(Vit , Kit , λit ) = PitV Vit + rit Kit + Fit − λit (Q(·) − Q̄it ),where P V is the price of variable costs, r is the cost of using capital, Fit is the ﬁxed cost, Q(·) isthe production technology, and λ is the Lagrange multiplier, respectively. Here, it is assumed thatthe prices of variable costs are given. The ﬁrst-order condition with respect to variable costs V is
as follows:
∂Lit
∂Q(·)
= PitV − λit
= 0.
∂Vit
∂Vit
Vit
By multiplying all terms by Q
, we obtain the production elasticity of variable cost V:
it

v
θit
≡

∂Q(·) Vit
1 PitV Vit=.
∂Vit Qit
λit Qit

The Lagrange multiplier λ is a measure of marginal cost, and if the markup is deﬁned as the ratio)(
of production price Pit to marginal cost µ = Pλ , then the markup can be derived as:vµit = θit

Pit Qit.
PitV Vit(1)

We beneﬁt from the production function approach because Equation (1) allows us to estimate
markups directly. The production function approach does not require setting up a model for eachﬁrm and imposing elasticities of substitution with respect to other products or returns to scale.66v
The estimation of the output elasticity θitis discussed in De Loecker et al. (2020). See De Loecker et al. (2020) for
more details.

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ESRI Discussion Paper Serires No.389"Market Power and Labor Shares : Evidence from Japanese Firm-Level Data"

4 Markups at the ﬁrm level
Using the ﬁrm-level markups we estimated, we present ﬁve facts about the evolution of the distribution and the patterns of markups.7
First, we report the average markup. We calculate the average markup as a weighted averageby revenue. Then, we use the share of revenue as the weight. Figure 1 shows the evolution of ourbaseline measure of average markups. Throughout the sample period, the weighted average andmedian markups have been stable at slightly above 1.0. Aggregate markups are close to one. The
evolution of average markups is partly similar to that of the United States. De Loecker et al. (2020)
document that the median of markups in the United States did not change, while the weighted
average began to increase to 61% in 2016. We conﬁrm the stability of markups when focusing on
markups at the industry level. Figure 2 shows that the evolution of average markups at the industry
level was stable from 1.0 to 1.2; while markups declined in Panel (5) Transportation and Postal
Services and Panel (7) Accommodation, Food and Drink Services, they are stable around 1.0 or 1.1
in the other industries.
Second, we present the evidence on the characteristics of the distribution. Our estimation strategy enables us to obtain markups for each ﬁrm. It reﬂects the underlying distributional characteristics of markups. A key ﬁnding is a right-skewed distribution, as shown in Figure 3. The ﬁgureplots the kernel density of the unweighted markups for 2009 and 2019 and the percentiles of themarkup distribution. We ﬁnd that the upper tail is long, whereas the variance is stable. As the kernel density does not consider the weights, we then plot the percentile markup distribution. We rank
the ﬁrms by markup to obtain the percentiles, where we weigh each ﬁrm by its market share in theentire sample. This makes the percentiles directly comparable to our share-weighted average. The
ranking is updated each year; therefore, the ﬁrms at the top may be different for each year. Figure
4 shows that all percentiles are invariant over time, while the upper tail is long. This conﬁrms aright-skewed distribution and no change in average markups at any percentile over the last decadein Japan.
Third, we ﬁnd that markups are negatively correlated with ﬁrm age. Figure 5 shows thatmarkups are lower with “old” ﬁrms than with the “young.” While the inverse U-shaped curve
below 40 years of ﬁrm age, the relationship between markups and ﬁrm age is basically negative.
The relationship is statistically signiﬁcant. Table 1 presents the result of regressing markups on
ﬁrm age. It suggests that an increase in age by 10% leads to a signiﬁcant decrease in markups by
0.2%. Columns (3) and (4) in the table show that the negative association is robust when the sample
7
We check the robustness of the ﬁve facts using surveys from the Financial Statements Statistics of Corporations andﬁnancial statements of the listed ﬁrms from Nikkei NEEDS. However, we do not report the evidence to save space.

6

ESRI Discussion Paper Serires No.389"Market Power and Labor Shares : Evidence from Japanese Firm-Level Data"is split into manufacturing and nonmanufacturing industries.
Fourth, we ﬁnd that markups are negatively correlated with ﬁrm size. We use revenue as a proxyfor ﬁrm size and draw a scatterplot. Figure 6 shows that markups are lower for large ﬁrm sizes thanfor small ﬁrm sizes. The negative association is robust when the sample is split into manufacturing
and nonmanufacturing industries. We demonstrate the negative relationship in manufacturing andnonmanufacturing industries by the circle and triangle points, respectively. The relationship is
statistically signiﬁcant. Table 1 presents the result of regressing markups on ﬁrm size. It suggeststhat an increase in ﬁrm size by 10% leads to a signiﬁcant decrease in markups by 0.5%. Columns(3) and (4) in the table show that the negative association is robust when the sample is split into
manufacturing and nonmanufacturing industries.
Fifth, we ﬁnd that markups are higher in nonmanufacturing industries than in manufacturingindustries, as shown in Figure 6. Panels (1) and (2) in Figure 2 show the average markups inmanufacturing and nonmanufacturing industries, respectively. The panels suggest higher markups
in nonmanufacturing industries, approximately by 0.15 points, than in manufacturing industries.8

5 Markups and labor sharesThus far, we have documented the time series and cross-sectional evolution of markups. We now
discuss the microeconomic dynamics and macroeconomic implications of the relationship between
markups and labor share.
In our setup, the change in markups impacts the change in the labor share; they are negatively
correlated. Rewriting the ﬁrst-order condition (1) where V = L, P V = w, and θV = θL , the output
elasticity of labor, the labor share satisﬁes
θL
wt Lit= it .
Pt Qitµit
Proﬁt maximization by individual ﬁrms implies that the labor share is inversely proportional to themarkup. Moreover, the negative relationship is a natural consequence of our setting. It follows
immediately from the ﬁrm’s optimization decision that high markups necessarily lead to lower
expenditure on inputs such as labor. In our setup, the rise in markups leads to a decrease in thelabor share.
To examine the association of ﬁrm-level markups with labor share, we plot it graphically.9 The
8
We conﬁrm that markups are signiﬁcantly higher in nonmanufacturing industries than manufacturing industries byregressing markups on industry dummies. However, we do not report the result to save space.
9
We compute the ﬁrm-level labor share. Our deﬁnition is the ratio of the total compensation of employees to the7

ESRI Discussion Paper Serires No.389"Market Power and Labor Shares : Evidence from Japanese Firm-Level Data"

top panel in Figure 7 shows the negative correlation between changes in the markup (d(M arkup))and the labor share (d(Labor share)). The ﬁgure implies that the rise in markups leads to adecrease in the labor share.
The negative relationship between changes in markups and the labor share is supported by the
positive relationship between the changes in markups and the capital share. We consider dividend
payouts to shareholders as a proxy of the capital share. The bottom panel in Figure 7 shows the positive correlation between changes in the markup (d(M arkup)) and dividends paid to shareholders(d(ln(Dividend))). We ﬁnd that the rise in markups coincides with a decrease in the labor shareand a rise in the capital share.
Then, we statistically test the inverse relationship. We regress the labor share and the dividends
paid to shareholders on the ﬁrm-level markup, controlling for ﬁrm-ﬁxed effects. Table 2 reports theresults from the regression of the labor share on the ﬁrm’s markup. In the table, we consistently
ﬁnd negative coefﬁcients. Table 2 suggests that a 10% rise in the markup leads to a 4% decreasein the labor share at the macro level. Columns (2) and (3) in the table suggest that the negative
relationship is robust when we focus on manufacturing and nonmanufacturing industries. While
we ﬁnd the negative relation between markups and the labor share, we ﬁnd the positive coefﬁcients
between markups and the capital share. Table 3 reports the results from the regression of the capitalshare on the ﬁrm’s markup. Table 3 suggests that a 10% rise in a ﬁrm’s markup is associatedwith an 8% increase in the dividend paid to shareholders at the macro level. Columns (2) and (3)in the table suggest that the positive relationship is robust when we focus on manufacturing andnonmanufacturing industries. To check for robustness, we regress the dividend ratio to revenue on
the ﬁrm-level markup. Table 4 presents the results. We consistently ﬁnd positive coefﬁcients. The
table presents the positive relationship between markups and capital shares in all cases. In addition,
it suggests that a 10% rise in a ﬁrm’s markup is associated with an 8.6% increase in the ratio at themacro level. Our results support the view that the rise in the markup causes a decrease in the labor
share and an increase in the capital share.

value added. Value added is the sum of operating income, total compensation of employees, and depreciation. Figures
A.1 and A.2 in Appendix show the evolution of our baseline measure of average labor shares at the macroeconomic andindustry levels, respectively. The (weighted) average of labor shares varies between 52% and 55%, while the median isapproximately 62% over the sample period. This value is not different from that found in the literature, e.g., Aoki et al.
(2023).

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ESRI Discussion Paper Serires No.389"Market Power and Labor Shares : Evidence from Japanese Firm-Level Data"

6 Discussion
Our results suggest no change in markups in Japan over the last decade. Although we ﬁnd that theweighted average and median of markups have been stable at slightly above 1.0 over the sample
period, the evolution of average markups is partially similar to that of the United States. De Loecker
et al. (2020) document that the median of markups in the United States is unchanging.
In contrast to the United States, there is no superstar ﬁrm effects in Japan. De Loecker et al.
(2020) demonstrate that the weighted average of markups began to rise to 61% in 2016. Theyﬁnd that the increase is primarily driven by the upper tail of the markup distribution; the upper
percentiles have risen sharply. This is not similar to the case in Japan. We ﬁnd that the upper
tail is long, whereas the variance has been stable. Our results suggest that there is no evidence inJapan of the so-called superstar ﬁrm effect with strong market power that Autor et al. (2020) ﬁnd.
This contrasts with the United States. Our evidence suggests that as is consistent with Porter andSakakibara (2006), competitiveness in Japan is ﬁerce.
Our results are slightly different from the literature that estimates ﬁrm-level markups in Japan.
Aoki et al. (2023) document the declining trend in markups. The dataset they use is a ﬁrm-level
dataset that includes those for large ﬁrms and small and medium-sized ﬁrms. The declining trend
documented in Aoki et al. (2023) imply that markups are higher for small ﬁrms than for large ﬁrms.
Figure 6 plots markups by revenue as a proxy for ﬁrm size. While our dataset does not include
data for small ﬁrms, the ﬁgure may suggest higher markups for smaller ﬁrms. If only small ﬁrms
experienced a decline in markups over the sample period, we would not observe a decline in averagemarkups. Further analysis using large data sets is left for our future research.10

7 Conclusion
We analyze the association between markups and labor share in Japan. We estimate markups asa proxy of market power and present empirical evidence on the patterns of markups. First, we
ﬁnd that aggregate markups are stable. It suggests no superstar ﬁrm effects, which Autor et al.
(2020) ﬁnd in the United States. Although markups are stable at the aggregate level, we present
evidence on their microeconomic dynamics. We ﬁnd a relationship between markups and ﬁrms’
demographics: markups are negatively associated with ﬁrm age and size. Thus, markups are higher
10
We use ﬁrm surveys such as the Annual Survey of Corporate Behavior conducted by the Economic and Social ResearchInstitute, Cabinet Ofﬁce of Japan, and Business Outlook Survey jointly conducted by the Economic and Social ResearchInstitute, Cabinet Ofﬁce of Japan, and Ministry of Finance to analyze the relationship between a ﬁrm’s markup and its
price-setting behavior. Although we obtain several interesting ﬁndings, we could not verify their robustness. It is also left
for our future research.

9

ESRI Discussion Paper Serires No.389"Market Power and Labor Shares : Evidence from Japanese Firm-Level Data"for young and small ﬁrms. We also ﬁnd the patterns of markups at the semi-macro level: markups
are higher for nonmanufacturing industries than for manufacturing industries. Furthermore, we discuss the macroeconomic implications of the patterns of markups. We demonstrate that the increasein market power entails a decline in labor share and an increase in capital share.
Our results suggest no change in markups in Japan over the last decade. Although we ﬁnd thatthe weighted average and median of markups have been stable at slightly above 1.0 over the sample
period, the evolution of average markups is partially similar to that of the United States. Moreover,unlike the United States, Japanese ﬁrms’ markups do not increase. Therefore, we ﬁnd that the upper
tail is long, while the variance is stable. Our evidence suggests that as is consistent with Porter andSakakibara (2006), competitiveness in Japan is ﬁerce.

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ESRI Discussion Paper Serires No.389"Market Power and Labor Shares : Evidence from Japanese Firm-Level Data"

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Yeh, Chen, Claudia Macaluso, and Brad Hershbein (2022), “Monopsony in the US Labor Market.”
American Economic Review 112(7): 2099–2138.

13

ESRI Discussion Paper Serires No.389"Market Power and Labor Shares : Evidence from Japanese Firm-Level Data"

Figure 1: Average markups (revenue weight)

1.5

Arithmetic (simple) mean
MedianWeighted average

1.4
1.3
1.2
1.1
1

.9
2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020

14

ESRI Discussion Paper Serires No.389"Market Power and Labor Shares : Evidence from Japanese Firm-Level Data"

Figure 2: Average markups at industry level (revenue weight)(1) Manufacturing(2) Nonmanufacturing

1.3

1.3

1.2

1.2

1.1

1.1

1

1

.9
2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020.92009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020

(3) Wholesale(4) Retail trade

1.3

1.3

1.2

1.2

1.1

1.1

1

1

.9
2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020.92009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020

(5) Transport and postal activities(6) Construction

1.3

1.3

1.2

1.2

1.1

1.1

1

1

.9
2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020.92009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020

(7) Accommodation, eating, and drinking services(8) Living-related and personal services and amusement services

1.3

1.3

1.2

1.2

1.1

1.1

1

1

.9
2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020.92009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020

15

ESRI Discussion Paper Serires No.389"Market Power and Labor Shares : Evidence from Japanese Firm-Level Data"

Figure 3: Distribution of markups: Kernel density (unweighted)
2.5

2009
2019

Density

2

1.5

1

.5

0
.5

1

1.5

2
markup16

2.5

3

ESRI Discussion Paper Serires No.389"Market Power and Labor Shares : Evidence from Japanese Firm-Level Data"

Figure 4: Distribution of markups: Percentiles markup distribution (revenue weight)

3

2.5

P99
P95
P90
P50
P25

2

1.5

1
2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020

17

ESRI Discussion Paper Serires No.389"Market Power and Labor Shares : Evidence from Japanese Firm-Level Data"

Figure 5: Markups and ﬁrm age
1.3

Markups

1.2

1.1

1
20

40

60
Age

18

80

100

ESRI Discussion Paper Serires No.389"Market Power and Labor Shares : Evidence from Japanese Firm-Level Data"

Figure 6: Revenue as a proxy of ﬁrm size and markups: Manufacturing and nonmanufacturing industries
1.6

Manufacturing
Nonmanufacturing

Markups

1.4

1.2

1

.8
20

21

22

23
ln(Revenue)

19

24

25

ESRI Discussion Paper Serires No.389"Market Power and Labor Shares : Evidence from Japanese Firm-Level Data"

Figure 7: Changes in the markup and labor share (top) and dividend (bottom): All industriesd(Labor share) (%)

5

0

-5
-.1

-.05

0
d(Markup)

.05

.1

-.1

-.05

0
d(Markup).05.1d(ln(Dividend)) (%)

10

5

0

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ESRI Discussion Paper Serires No.389"Market Power and Labor Shares : Evidence from Japanese Firm-Level Data"

Table 1: Regression of markups on revenue: ﬁrm size effectsln(Revenue)(1)ln(M arkup)
Entire sample(2)ln(M arkup)
Entire sample(3)(4)
ln(M arkup)ln(M arkup)
Manufacturing Nonmanufacturing−0.0555***(0.000245)−0.0483***(0.000256)−0.0490***(0.000331)−0.0348***(0.000326)−0.0179***(0.000684)−0.0047***(0.000915)−0.0213***(0.000794)

YES
YES

YES
YES

YES
YES

ln(Age)Year ﬁxed effectIndustry ﬁxed effect

YESNO

Observations
326,564
300,363
134,291
110,815∗ ∗∗∗∗∗Notes: , , andindicate 10%, 5%, and 1% signiﬁcance, respectively. Robust standard errors are in parentheses.

21

ESRI Discussion Paper Serires No.389"Market Power and Labor Shares : Evidence from Japanese Firm-Level Data"

Table 2: Regression of labor shares on markups(1)(2)
ln(Labor share) ln(Labor share)
Entire sample
Manufacturingln(M arkup)ln(Revenue)

Year ﬁxed effectIndustry ﬁxed effectFirm ﬁxed effect(3)ln(Labor share)
Nonmanufacturing−0.397***(0.0347)−0.242***(0.00921)−0.716***(0.0279)−0.311***(0.0106)−0.710***(0.0592)−0.199***(0.0143)

YES
YES
YES

YES
YES
YES

YES
YES
YES

Observations
314,660
141,667
116,644
Notes: The ﬁrm-level labor share is the ratio of total compensation of employees to value added. Value added is the sum of operating income, totalcompensation of employees, and depreciation expense. ∗ , ∗∗ , and ∗∗∗ indicate 10%, 5%, and 1% signiﬁcance, respectively. Robust standard errors inparentheses are clustered at individual levels.

22

ESRI Discussion Paper Serires No.389"Market Power and Labor Shares : Evidence from Japanese Firm-Level Data"

Table 3: Regression of dividends paid out to shareholders on markups
(1)
(2)
(3)
ln(Dividend) ln(Dividend)ln(Dividend)
Entire sample Manufacturing Nonmanufacturingln(M arkup)ln(Revenue)

Year ﬁxed effectIndustry ﬁxed effectFirm ﬁxed effect0.799***(0.0366)
0.767***(0.0306)

1.138***(0.0558)
0.786***(0.0152)

1.478***(0.0712)0.649***(0.0316)

YES
YES
YES

YES
YES
YES

YES
YES
YES

Observations
165,836
74,281
58,221∗ ∗∗∗∗∗Notes: , , andindicate 10%, 5%, and 1% signiﬁcance, respectively.
Robust standard errors in parentheses are clustered at individual levels.

23

ESRI Discussion Paper Serires No.389"Market Power and Labor Shares : Evidence from Japanese Firm-Level Data"

Table 4: Regression of the ratio of dividends to revenue on markups(1) )( Dividend(2) )( Dividend(3) )( Dividend

0.857***(0.0336)

1.167***(0.0547)

1.576***(0.0793)

YES
YES
YES

YES
YES
YES

YES
YES
YES

ln Revenue
Entire sampleln(M arkup)

Year ﬁxed effectIndustry ﬁxed effectFirm ﬁxed effectln Revenue
ln Revenue
Manufacturing Nonmanufacturing

Observations
165,836
74,281
58,221
Notes: ∗ , ∗∗ , and ∗∗∗ indicate 10%, 5%, and 1% signiﬁcance, respectively.
Robust standard errors in parentheses are clustered at individual levels.

24

ESRI Discussion Paper Serires No.389"Market Power and Labor Shares : Evidence from Japanese Firm-Level Data"

Appendix
Average labor shares
Figure A.1: Average labor shares (revenue weight)

70

Arithmetic (simple) mean
MedianWeighted averageLabor share (%)

65

60

55

50
2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020

25

ESRI Discussion Paper Serires No.389"Market Power and Labor Shares : Evidence from Japanese Firm-Level Data"

Figure A.2: Average labor shares at industry level (revenue weight)(1) Manufacturing(2) Nonmanufacturing

.75

.75

.7

.7

.65

.65

.6

.6

.55

.55

.5

.5

.45

.45

.4
2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020.42009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020

(3) Wholesale(4) Retail trade

.75

.75

.7

.7

.65

.65

.6

.6

.55

.55

.5

.5

.45

.45

.4
2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020.42009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020

(5) Transport and postal activities(6) Construction

.75

.75

.7

.7

.65

.65

.6

.6

.55

.55

.5

.5

.45

.45

.4
2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020.42009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020

(7) Accommodation, eating, and drinking services(8) Living-related and personal services and amusement services

.75

.75

.7

.7

.65

.65

.6

.6

.55

.55

.5

.5

.45

.45

.4
2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020.42009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 202026